03. Convolutional Neural Networks and Computer Vision with TensorFlow¶
We've done some basic TensorFlow stuff, along with some models. Let's get specific and learn a special kind of neural network called convolutional neural networks (CNNs), which helps detect patterns in visual data.
Note: Many different kinds of model architecture can be used for different problems in deep learning. You can use CNN in image data and text data, though there are some architectures that work better in other problems. So don't rely on one specific method.
For example:
- Classify whether a picture is a pizza 🍕 or steak 🥩 (we'd be doing this)
- Detect whether an object appears in an image (did a car just pass by the dashcam?)
What's covered:¶
- Getting a dataset to work with
- Architecture of a CNN (Convolutional Neural Network)
- A quick end-to-end example (what we're working towards)
- Steps in modelling for binary image classification in CNN
- Becoming one with the data
- Prepearing data for modelling
- Creating a CNN model (starting with a baseline)
- Fitting a model (getting it to find patterns in our data)
- Evaluating a model
- Improving a model
- Making a prediction with a trained model
- Steps in modelling for multi class image classification with CNN
- Same as above (but with a different dataset)
In [2]:
import datetime
print(f"Notebook last run (end-to-end): {datetime.datetime.now()}")
Notebook last run (end-to-end): 2025-05-21 10:08:00.984057
Get the data¶
CNNs work very well with image data, and we're going to use image dataset to learn about them.
Image dataset will come from Food-101, a collection of 101 different categories of 101,000 real-world images of food dishes.
To begin, we'll create a binary classifier for pizza 🍕 and steak 🥩
there is a zip file created for pizza and steak, so no pre-processing is currently needed now.
In [4]:
import zipfile
import urllib.request
# Step 1: Download the zip file
url = "https://storage.googleapis.com/ztm_tf_course/food_vision/pizza_steak.zip"
urllib.request.urlretrieve(url, "pizza_steak.zip") # saves the file locally
# Step 2: Unzip the file
with zipfile.ZipFile("pizza_steak.zip", "r") as zip_ref:
zip_ref.extractall() # extract all files to the current directory
Inpect the data (become one with it)¶
Its a very crucial step, where it usually is meant by visualizing and scanning through the folder of data you're working with. Try and understand what it is you're dealing with.
The file structure is formatted to what you may typically see when working with image datasets.
Example of what it looks like:
pizza_steak <- top level folder
└───train <- training images
│ └───pizza
│ │ │ 1008104.jpg
│ │ │ 1638227.jpg
│ │ │ ...
│ └───steak
│ │ 1000205.jpg
│ │ 1647351.jpg
│ │ ...
│
└───test <- testing images
│ └───pizza
│ │ │ 1001116.jpg
│ │ │ 1507019.jpg
│ │ │ ...
│ └───steak
│ │ 100274.jpg
│ │ 1653815.jpg
│ │ ...
Let's inspect the directory, which can be done with listdir, short for list directory. Though first, we must import os
In [16]:
import os
os.listdir('pizza_steak')
Out[16]:
['test', 'train']
In [17]:
os.listdir('pizza_steak/train/')
Out[17]:
['steak', 'pizza']
In [18]:
files = os.listdir('pizza_steak/train/steak/')
cols = 10
# Print files in a grid
for i in range(0, len(files), cols):
print(" ".join(files[i:i+cols]))
239025.jpg 1155665.jpg 3007772.jpg 1598345.jpg 658189.jpg 172936.jpg 3807440.jpg 168775.jpg 331860.jpg 2939678.jpg 2173084.jpg 1327667.jpg 468384.jpg 3074367.jpg 1487113.jpg 2568848.jpg 143490.jpg 2233395.jpg 3009617.jpg 2995169.jpg 1567554.jpg 268444.jpg 1403005.jpg 637374.jpg 2390628.jpg 2172600.jpg 2761427.jpg 3621464.jpg 3540750.jpg 134369.jpg 2832499.jpg 3253588.jpg 2291292.jpg 3260624.jpg 2090493.jpg 1219039.jpg 140832.jpg 955466.jpg 168006.jpg 2691461.jpg 1658443.jpg 786409.jpg 669180.jpg 3700079.jpg 1777107.jpg 2412263.jpg 2940621.jpg 1524526.jpg 3750472.jpg 3470083.jpg 413325.jpg 2979061.jpg 1093966.jpg 914570.jpg 2984311.jpg 361067.jpg 4176.jpg 669960.jpg 2779040.jpg 3393547.jpg 3829392.jpg 3556871.jpg 2394465.jpg 1971757.jpg 1548239.jpg 167069.jpg 217250.jpg 231296.jpg 3110387.jpg 1100074.jpg 101312.jpg 614975.jpg 3770370.jpg 3916407.jpg 1147883.jpg 2532239.jpg 417368.jpg 2724554.jpg 640539.jpg 3396589.jpg 644777.jpg 3002350.jpg 568972.jpg 2088195.jpg 2133717.jpg 1636831.jpg 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In [19]:
files = os.listdir('pizza_steak/train/pizza/')
cols=10
for i in range(0,len(files),cols):
print(" ".join(files[i:i+cols]))
2577377.jpg 102037.jpg 384215.jpg 1033251.jpg 2312987.jpg 3057192.jpg 2501961.jpg 132484.jpg 1888911.jpg 3426946.jpg 2572958.jpg 2700543.jpg 143453.jpg 166823.jpg 670201.jpg 2667255.jpg 1763205.jpg 387697.jpg 375401.jpg 89892.jpg 29417.jpg 1083380.jpg 1663749.jpg 1499661.jpg 2293453.jpg 448519.jpg 928670.jpg 169720.jpg 1906287.jpg 2021516.jpg 1686908.jpg 792093.jpg 1839077.jpg 1325918.jpg 1207213.jpg 2126709.jpg 3170114.jpg 3066951.jpg 1968947.jpg 816577.jpg 1504421.jpg 2778214.jpg 951953.jpg 169318.jpg 2410138.jpg 3473991.jpg 1786840.jpg 2241448.jpg 1934355.jpg 63480.jpg 2952219.jpg 300869.jpg 1576248.jpg 299535.jpg 3434372.jpg 3256974.jpg 1617418.jpg 3910117.jpg 618021.jpg 3702863.jpg 2881282.jpg 2821034.jpg 2885796.jpg 2104569.jpg 2097315.jpg 2902766.jpg 3456440.jpg 3392671.jpg 369017.jpg 131561.jpg 724445.jpg 2739100.jpg 489347.jpg 1717790.jpg 1681043.jpg 2467990.jpg 3366256.jpg 709273.jpg 962785.jpg 3574192.jpg 3478964.jpg 2291093.jpg 1089334.jpg 368644.jpg 768276.jpg 1705747.jpg 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There's a lot of images, but how many?
In [20]:
# walk through pizza_steak directory and list number of files
for dirpath, dirnames, filenames in os.walk('pizza_steak'):
print(f"There are {len(dirnames)} directories and {len(filenames)} images in `{dirpath}`")
There are 2 directories and 0 images in `pizza_steak` There are 2 directories and 0 images in `pizza_steak\test` There are 0 directories and 250 images in `pizza_steak\test\steak` There are 0 directories and 250 images in `pizza_steak\test\pizza` There are 2 directories and 0 images in `pizza_steak\train` There are 0 directories and 750 images in `pizza_steak\train\steak` There are 0 directories and 750 images in `pizza_steak\train\pizza`
In [21]:
# another way to find number of images in a folder
num_steak_images_train = len(os.listdir('pizza_steak/train/steak/'))
num_steak_images_train
Out[21]:
750
In [22]:
# get list of class names (very useful if dealing with many categories and classes)
import pathlib
import numpy as np
data_dir = pathlib.Path('pizza_steak/train/') # turn our training path into python path
# it turns our folder string, into a path object. A flexible way to work with file paths in python
class_names = np.array(sorted([item.name for item in data_dir.glob('*')])) # created a list of class_names from the subdirectories
# data_dir.glob > gets all items in the directory
# item.name for item > extracts only the name of each item
# sorted(...) > sorts name by alphabetical order
# np.array(...) > converts sorted list to Numpy array for efficient processing
class_names
Out[22]:
array(['pizza', 'steak'], dtype='<U5')
Based on above info, we've got 750 training images, and 250 test images of either steak or pizza.
Now let's visualize one of the images
In [23]:
# view an image
import matplotlib.pyplot as plt
import matplotlib.pyplot as mpimg
import random
def view_random_image(target_dir, target_class):
# setup target directory
target_folder = target_dir+target_class
# get a random image path
random_image = random.sample(os.listdir(target_folder), 1) # the 1, states to only pick `one` image
# read in the image and plot it using matplotlib
img = mpimg.imread(target_folder + '/' + random_image[0]) # [0], as img gets a list, we can't append it to directory unless it is a string. So you go into the list and do `[0]` for the first and only result
plt.imshow(img)
plt.title(target_class)
plt.axis('off');
print(f"Image shape: {img.shape}")
return img
In [25]:
# view a random image from the training dataset
img = view_random_image(target_dir='pizza_steak/train/',
target_class='steak')
Image shape: (512, 512, 3)
You might notice we've printed the image shape with the actual image. This is due to how the computer sees the image, through a big array (tensor).
In [37]:
# how the computer views img
img
Out[37]:
array([[[226, 156, 44],
[220, 150, 38],
[207, 137, 23],
...,
[219, 170, 173],
[212, 163, 166],
[220, 171, 174]],
[[223, 155, 46],
[219, 151, 40],
[212, 144, 33],
...,
[209, 160, 163],
[208, 159, 162],
[212, 163, 166]],
[[211, 146, 42],
[209, 145, 39],
[211, 147, 41],
...,
[207, 159, 159],
[209, 161, 161],
[210, 162, 162]],
...,
[[224, 192, 171],
[227, 195, 174],
[228, 196, 175],
...,
[233, 207, 190],
[236, 211, 191],
[242, 217, 197]],
[[222, 192, 168],
[226, 196, 172],
[228, 197, 176],
...,
[234, 208, 191],
[238, 213, 193],
[244, 219, 199]],
[[225, 195, 171],
[229, 199, 175],
[229, 198, 177],
...,
[230, 204, 187],
[232, 207, 187],
[234, 209, 189]]], dtype=uint8)
In [38]:
img.shape # image shape, returning (width, height, colour channel)
Out[38]:
(512, 512, 3)
You see our image shape as a 3d array of width, height, colour channel. Width and height of image in our dataset may vary, though we will always have 3 colour channels, representing red, blue, and green.
Our img array also contains value between 0 to 255, as that's the possible range for red, blue, green, and also grayscale images as well.
So when building a model to differentiate pizza and steak, it will find patterns in these different pixel values to determine which class is which.
Note: Values are between 0 and 255, but ML would much prefer values between 0 and 1, so we'll need to scale/normalize it, by dividing it with 255.
In [39]:
# get all values within 0 to 1
img/255.0
Out[39]:
array([[[0.88627451, 0.61176471, 0.17254902],
[0.8627451 , 0.58823529, 0.14901961],
[0.81176471, 0.5372549 , 0.09019608],
...,
[0.85882353, 0.66666667, 0.67843137],
[0.83137255, 0.63921569, 0.65098039],
[0.8627451 , 0.67058824, 0.68235294]],
[[0.8745098 , 0.60784314, 0.18039216],
[0.85882353, 0.59215686, 0.15686275],
[0.83137255, 0.56470588, 0.12941176],
...,
[0.81960784, 0.62745098, 0.63921569],
[0.81568627, 0.62352941, 0.63529412],
[0.83137255, 0.63921569, 0.65098039]],
[[0.82745098, 0.57254902, 0.16470588],
[0.81960784, 0.56862745, 0.15294118],
[0.82745098, 0.57647059, 0.16078431],
...,
[0.81176471, 0.62352941, 0.62352941],
[0.81960784, 0.63137255, 0.63137255],
[0.82352941, 0.63529412, 0.63529412]],
...,
[[0.87843137, 0.75294118, 0.67058824],
[0.89019608, 0.76470588, 0.68235294],
[0.89411765, 0.76862745, 0.68627451],
...,
[0.91372549, 0.81176471, 0.74509804],
[0.9254902 , 0.82745098, 0.74901961],
[0.94901961, 0.85098039, 0.77254902]],
[[0.87058824, 0.75294118, 0.65882353],
[0.88627451, 0.76862745, 0.6745098 ],
[0.89411765, 0.77254902, 0.69019608],
...,
[0.91764706, 0.81568627, 0.74901961],
[0.93333333, 0.83529412, 0.75686275],
[0.95686275, 0.85882353, 0.78039216]],
[[0.88235294, 0.76470588, 0.67058824],
[0.89803922, 0.78039216, 0.68627451],
[0.89803922, 0.77647059, 0.69411765],
...,
[0.90196078, 0.8 , 0.73333333],
[0.90980392, 0.81176471, 0.73333333],
[0.91764706, 0.81960784, 0.74117647]]])
A (typical) architecture of a convolutional neural network¶
CNN are no different than other deep learning neural networks, besides being able to be created in many different ways. Here's a list of components in a network:
| Hyperparameter/layer type | What it does? | Typical values |
|---|---|---|
| Input image(s) | Target images you want to discover patterns of | Any type of photo (or video) |
| Input layer💙 | Takes target image, and preprocesses them for further layers | input_shape = [batch_size, img_height, img_width, channels] |
| Convolutional layer💧 | Extracts/learns most important features in image | Multiple, can create with tf.keras.layers.ConvXD (X can be multiple values) |
| Hidden activation💚 | Adds non-linearity to learned features (non-straight lines) | Usually ReLU (tf.keras.activations.relu) |
| Pooling layer💛 | Reduces dimensionality/size of learned image features | Average (tf.keras.layers.AvgPool2D) or Max(`tf.keras.layers.MaxPool2D) |
| Fully connected layer🧡 | Further refines learned features from convolution layers | tf.keras.layers.Dense |
| Output layer🧡 | Takes learned features and outputs them in shape of target labels | output_shape = [number_of_classes] (e.g. 3 for pizza, steak or sushi) |
| Output Activation🖤 | Adds non-linearities to output layer | tf.keras.activations.sigmoid (binary classification) or tf.keras.activations.softmax |
A typical CNN model, colour coded to its respective layer type
A simple example of how you might stack together the above layers into a convolutional neural network. Note the convolutional and pooling layers can often be arranged and rearranged into many different formations
An end-to-end example¶
We've seen how there's 750 training, and 250 testing images of either class. It's time to jump to the deep end.
There's an original dataset author papers, where they stated their use of Random Forest machine learning models, averaging an accuracy of 50.76% when predicting different foods.
50.76% Will be our baseline to achieve.
Note: A baseline is a score/evaluaion metric you want to beat. Usually you start with a simple model, create a baseline based on that, and beat it through increasing complexity of model. A fun way to get your baseline is through some modelling paper with publiched results.
The following code underneath, replicates an end-to-end model for pizza_steak dataset, inclusive of CNN as it uses the components that has been explained above. We'll go through each steps later in the notebook.
The model used replicates TinyVGG. The computer vision architecture which fuels the CNN explainer webpage.
Resource: Architecture we're using is a scaled down version of
VGG-16
In [1]:
!pip install tensorflow
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In [3]:
!pip install Pillow
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In [1]:
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# Set the seed
tf.random.set_seed(42)
# Preprocess data (get all of the pixel values between 1 and 0, also called scaling/normalization)
train_datagen = ImageDataGenerator(rescale=1./255)
valid_datagen = ImageDataGenerator(rescale=1./255)
# Setup the train and test directories
train_dir = "pizza_steak/train/"
test_dir = "pizza_steak/test/"
# Import data from directories and turn it into batches
train_data = train_datagen.flow_from_directory(train_dir,
batch_size=32, # number of images to process at a time
target_size=(224, 224), # convert all images to be 224 x 224
class_mode="binary", # type of problem we're working on
seed=42)
valid_data = valid_datagen.flow_from_directory(test_dir,
batch_size=32,
target_size=(224, 224),
class_mode="binary",
seed=42)
# Create a CNN model (same as Tiny VGG - https://poloclub.github.io/cnn-explainer/)
model_1 = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(filters=10,
kernel_size=3, # can also be (3, 3)
activation="relu",
input_shape=(224, 224, 3)), # first layer specifies input shape (height, width, colour channels)
tf.keras.layers.Conv2D(10, 3, activation="relu"),
tf.keras.layers.MaxPool2D(pool_size=2, # pool_size can also be (2, 2)
padding="valid"), # padding can also be 'same'
tf.keras.layers.Conv2D(10, 3, activation="relu"),
tf.keras.layers.Conv2D(10, 3, activation="relu"), # activation='relu' == tf.keras.layers.Activations(tf.nn.relu)
tf.keras.layers.MaxPool2D(2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(1, activation="sigmoid") # binary activation output
])
# Compile the model
model_1.compile(loss="binary_crossentropy",
optimizer=tf.keras.optimizers.Adam(),
metrics=["accuracy"])
# Fit the model
history_1 = model_1.fit(train_data,
epochs=5,
steps_per_epoch=len(train_data),
validation_data=valid_data,
validation_steps=len(valid_data))
Found 1500 images belonging to 2 classes. Found 500 images belonging to 2 classes.
x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\layers\convolutional\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs) x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\trainers\data_adapters\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored. self._warn_if_super_not_called()
Epoch 1/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 27s 551ms/step - accuracy: 0.6127 - loss: 0.6567 - val_accuracy: 0.8200 - val_loss: 0.4221 Epoch 2/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 9s 182ms/step - accuracy: 0.7963 - loss: 0.4422 - val_accuracy: 0.8440 - val_loss: 0.3758 Epoch 3/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 9s 182ms/step - accuracy: 0.7963 - loss: 0.4386 - val_accuracy: 0.8660 - val_loss: 0.3287 Epoch 4/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 9s 184ms/step - accuracy: 0.8481 - loss: 0.3722 - val_accuracy: 0.8580 - val_loss: 0.3748 Epoch 5/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 9s 185ms/step - accuracy: 0.8452 - loss: 0.3756 - val_accuracy: 0.8700 - val_loss: 0.3321
Nice, we've achieved 87% accuracy in validation, way above the 50.78% initial goal! The model is a binary classification model, than 101 different categories. At least it shows the model being able to learn things.
Since we've already fit a model, let's check out its architecture.
In [2]:
# check out the layers in our model
model_1.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 222, 222, 10) │ 280 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_1 (Conv2D) │ (None, 220, 220, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 110, 110, 10) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_2 (Conv2D) │ (None, 108, 108, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_3 (Conv2D) │ (None, 106, 106, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 53, 53, 10) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ flatten (Flatten) │ (None, 28090) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ (None, 1) │ 28,091 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 93,305 (364.48 KB)
Trainable params: 31,101 (121.49 KB)
Non-trainable params: 0 (0.00 B)
Optimizer params: 62,204 (242.99 KB)
With the layers in model_1, a lot of them can be understood through this CNN explainer website. Talking about convolutional layers, what they do, their activation functions, pooling, and flatten layers.
However there are a few others that haven't been discussed, namely:
- The
ImageDataGeneratorclass and therescaleparameter - The
flow_from_directory()method- The
batch_sizeparameter - The
target_sizeparameter
- The
Conv2Dlayers (and the parameters which come with them)MaxPool2Dlayers (and their parameters)- The
steps_per_epochandvalidation_stepsparameters in thefit()function
Before learning these, let's try fit a model we've worked on previously to our data.
Using the same model as before¶
To create an example of how neural networks can adapt to different problems, let's check out the binary classification model, and how it might work with our data.
We can use all the same parameters except two things:
- The data - we'll be working with images instead of dots
- The input shape - we need to tell the neural network, the shape of the image it will be working with.
- Common practice is resizing all images to one size. For us, we'll resize to (224, 224, 3), aka an image width/height of 224 pixels, with 3 colour channels (RGB).
In [4]:
# set random seed
tf.random.set_seed(42)
# create a model to replicate the Tensorflow Playground model
model_2 = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(224,224,3)),
tf.keras.layers.Dense(4, activation='relu'),
tf.keras.layers.Dense(4, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
# compile the model
model_2.compile(loss='binary_crossentropy',
optimizer=tf.keras.optimizers.Adam(),
metrics=['accuracy'])
# fit the model
history_2 = model_2.fit(train_data,
epochs=5,
steps_per_epoch=len(train_data),
validation_data=valid_data,
validation_steps=len(valid_data))
x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\layers\reshaping\flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
Epoch 1/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 21s 423ms/step - accuracy: 0.4962 - loss: 0.7066 - val_accuracy: 0.5000 - val_loss: 0.6931 Epoch 2/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 5s 96ms/step - accuracy: 0.5518 - loss: 0.6847 - val_accuracy: 0.5000 - val_loss: 0.6932 Epoch 3/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 94ms/step - accuracy: 0.5259 - loss: 0.6743 - val_accuracy: 0.6380 - val_loss: 0.6128 Epoch 4/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 96ms/step - accuracy: 0.6336 - loss: 0.6272 - val_accuracy: 0.5000 - val_loss: 0.7052 Epoch 5/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 93ms/step - accuracy: 0.4978 - loss: 0.7000 - val_accuracy: 0.5000 - val_loss: 0.6931
Hmm, don't think the model has learnt anything, only reaching 50% accuracy in the training and test set. In binary classification, that's as good as if it guessed.
In [5]:
# check the second model's architecture
model_2.summary()
Model: "sequential_2"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ flatten_2 (Flatten) │ (None, 150528) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_4 (Dense) │ (None, 4) │ 602,116 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_5 (Dense) │ (None, 4) │ 20 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_6 (Dense) │ (None, 1) │ 5 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 1,806,425 (6.89 MB)
Trainable params: 602,141 (2.30 MB)
Non-trainable params: 0 (0.00 B)
Optimizer params: 1,204,284 (4.59 MB)
It's clear that the model has a much larger number of parameters compared to model_1.
2 has 600k+ trainable params, while 1 has 30k+ params, yet outperforms 2.
Note: trainable parameters are basically patterns for a model to learn from the data. You may think more is better, but its not always the case. The difference is the styles of models used. A series of
Denselayers have a number of different learnable parameters thats connected to each other (which increases the number of possible learnable patterns). But aCNNmodel, learn the most important patterns in an image.
Since model_2 didn't work, in what way can we make it work?
Should we increase the number of layers? Maybe the increase of neurons per layer?
More specifically, we'll increase the number of neurons (also called hidden units) in each dense layer from 4 to 100, and add an extra layer.
Note: Adding extra layers/increasing number of neurons isknown as increasing the complexity of your model.
In [6]:
# set random seed
tf.random.set_seed(42)
# create a model similar to model_1 but add an extra layer and increase the number of hidden units in each layer
model_3 = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(224,224,3)), # dense layers expect a 1-dimensional vector as input
tf.keras.layers.Dense(100, activation='relu'), # increase the number of neurons from 4 to 100 (for every layer)
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
# Compile the model
model_3.compile(loss='binary_crossentropy',
optimizer=tf.keras.optimizers.Adam(),
metrics=['accuracy'])
# Fit the model
history_3 = model_3.fit(train_data,
epochs=5,
steps_per_epoch=len(train_data),
validation_data=valid_data,
validation_steps=len(valid_data))
x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\layers\reshaping\flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
Epoch 1/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 10s 188ms/step - accuracy: 0.6247 - loss: 6.6889 - val_accuracy: 0.6380 - val_loss: 1.3323 Epoch 2/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 9s 187ms/step - accuracy: 0.6997 - loss: 0.9636 - val_accuracy: 0.6620 - val_loss: 1.2410 Epoch 3/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 8s 177ms/step - accuracy: 0.7238 - loss: 0.9987 - val_accuracy: 0.6800 - val_loss: 1.0702 Epoch 4/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 8s 172ms/step - accuracy: 0.7212 - loss: 0.7914 - val_accuracy: 0.7520 - val_loss: 0.5467 Epoch 5/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 8s 174ms/step - accuracy: 0.7101 - loss: 0.8614 - val_accuracy: 0.7580 - val_loss: 0.4529
Model is definitely learning something, with 71% accuracy, and 75% accuracy in validation dataset!
Let's check out architecture again
In [8]:
# check out model_3 architecture
model_3.summary()
Model: "sequential_3"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ flatten_3 (Flatten) │ (None, 150528) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_7 (Dense) │ (None, 100) │ 15,052,900 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_8 (Dense) │ (None, 100) │ 10,100 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_9 (Dense) │ (None, 1) │ 101 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 45,189,305 (172.38 MB)
Trainable params: 15,063,101 (57.46 MB)
Non-trainable params: 0 (0.00 B)
Optimizer params: 30,126,204 (114.92 MB)
Despite 15 million parameters, model_1 still outperforms it with it's measly 30k parameters. Goes to show the power of CNNs, and their ability to learn patterns with less parameters.
Binary classification: Let's break it down¶
1. Become one with the data¶
Whatever type of data you're working with, you want to at least visualize 10-100 samples to start to building your own mental model of the data.
For our case, we may notice the steak images tend to have darker colours, and include side dishes with it. Where pizza tends to have a distict circular shape. These are the patterns our neural network may pick up as well.
You can also notice if some of your data is messed up (wrong label), and consider ways to fix it.
In [11]:
!pip install matplotlib
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In [31]:
# visualize data (requires function 'view_random_image' above)
import matplotlib.pyplot as plt
plt.figure()
plt.subplot(1,2,1)
steak_img = view_random_image('pizza_steak/train/','steak')
plt.subplot(1,2,2)
pizza_img = view_random_image('pizza_steak/train/','pizza')
Image shape: (512, 382, 3) Image shape: (384, 512, 3)
2. Preprocess the data (prepare it for a model)¶
The most important step is creating a training and test set for the model. For us, the data has already been split between training and test. Another option could be to make a validation set, but we won't implement it now.
It's standard to separate train and test in it's own directories/folders, in each of their own classes.
To start, we define the train/test directory paths.
In [32]:
# define training and test directory paths
train_dir = 'pizza_steak/train/'
test_dir = 'pizza_steak/test/'
Next step is to turn data into batches.
A batch is a small subset of the dataset, the model looks at during training. Instead of 10,000 results at a time to figure out the patterns between all of them, they may look at 32 at a time instead.
It does this for a few reasons:
- 10,000 images or more, may not fit into memory of the GPU
- Trying to learn the patterns in 10,000 images in one go, can result in model to not learn very well.
Why 32? It's just simply better for model prediction. This is demonstrated by this Wilcon and Martinez's benchmark test
The results show how the smaller the batch size, the better the performance often is. Never go above 32!
To turn our data into catches, we'll create an instance of ImageDataGenerator for each of our datasets.
In [33]:
# create train an dtest data generators and rescale the data
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1/255.)
test_datagen = ImageDataGenerator(rescale=1/255.)
The ImageDataGenerator class helps prepare our images into batches, as well as perform transformations on them as they get loaded into the model.
You might've noticed the rescale parameter, and is an example of transformation we're doing. 255 is the number seen for colour values, or more so it's 'max'. Neural networks only really like to work with values between 0 to 1, and perform it's best when values are scaled in between that. So we'll divide the pixel value by it's max of 255, so even pixel values at max will be 1, fit for neural networks.
We can load our images from their respective directories using the flow_from_directories method.
In [34]:
# turn it into batches
train_data = train_datagen.flow_from_directory(directory=train_dir,
target_size=(224,224),
class_mode='binary',
batch_size=32)
test_data = test_datagen.flow_from_directory(directory=test_dir,
target_size=(224,224),
class_mode='binary',
batch_size=32)
Found 1500 images belonging to 2 classes. Found 500 images belonging to 2 classes.
Now we have 1500 images belonging to 2 classes (pizza and steak), and 500 images to the same 2 classes.
Some things to note:
- Because of the directory structure, the classes are inferenced by the subdirectory names in train/test folders.
- The
target_sizeparameter defines input size based on (width, height) format. - The
class_modevalue of 'binary' defines our classification problem type. If more than two categories, it becomes 'categorical'. - The
batch_sizedefines how many images will be in each batch. We're currently using 32 as default.
We can take a look at our batched images and labels by inspecting the train_data object.
In [41]:
# get a sample of the training data
images, labels = next(train_data) # getting the next batch of images
len(images), len(labels)
Out[41]:
(32, 32)
Seems our data is in 32 batches as it should be.
Let's see the image's appearance
In [42]:
# get the first two images
images[:2], images[0].shape
Out[42]:
(array([[[[0.56078434, 0.63529414, 0.79215693],
[0.5647059 , 0.6392157 , 0.7960785 ],
[0.5647059 , 0.6392157 , 0.80392164],
...,
[0.07843138, 0.08235294, 0.05882353],
[0.08235294, 0.08235294, 0.07450981],
[0.09803922, 0.09803922, 0.09803922]],
[[0.5647059 , 0.6392157 , 0.7960785 ],
[0.5568628 , 0.6313726 , 0.7960785 ],
[0.5568628 , 0.6313726 , 0.7960785 ],
...,
[0.09803922, 0.10196079, 0.07058824],
[0.0627451 , 0.06666667, 0.04705883],
[0.04313726, 0.04313726, 0.03529412]],
[[0.5686275 , 0.6431373 , 0.8078432 ],
[0.5647059 , 0.6392157 , 0.80392164],
[0.5647059 , 0.6392157 , 0.8078432 ],
...,
[0.07450981, 0.07843138, 0.04705883],
[0.15686275, 0.16078432, 0.13725491],
[0.21568629, 0.21960786, 0.20000002]],
...,
[[0.3921569 , 0.34901962, 0.22352943],
[0.39607847, 0.3529412 , 0.23529413],
[0.3372549 , 0.28235295, 0.1764706 ],
...,
[0.5372549 , 0.5294118 , 0.5803922 ],
[0.5372549 , 0.5294118 , 0.5803922 ],
[0.53333336, 0.5254902 , 0.5764706 ]],
[[0.38431376, 0.34901962, 0.23529413],
[0.34117648, 0.30588236, 0.19215688],
[0.16862746, 0.12941177, 0.03137255],
...,
[0.5372549 , 0.5294118 , 0.58431375],
[0.5372549 , 0.5294118 , 0.58431375],
[0.5411765 , 0.5254902 , 0.5803922 ]],
[[0.17254902, 0.14901961, 0.05490196],
[0.22352943, 0.20000002, 0.10588236],
[0.21176472, 0.18039216, 0.09019608],
...,
[0.5254902 , 0.5137255 , 0.5803922 ],
[0.5294118 , 0.5137255 , 0.57254905],
[0.5294118 , 0.5137255 , 0.5686275 ]]],
[[[0.31764707, 0.39607847, 0.5019608 ],
[0.38431376, 0.46274513, 0.56078434],
[0.34117648, 0.427451 , 0.5176471 ],
...,
[0.31764707, 0.24705884, 0.24705884],
[0.28627452, 0.21176472, 0.21960786],
[0.27058825, 0.19607845, 0.20392159]],
[[0.27450982, 0.29803923, 0.43137258],
[0.3137255 , 0.3372549 , 0.46274513],
[0.3019608 , 0.3372549 , 0.45098042],
...,
[0.32156864, 0.25882354, 0.25882354],
[0.3019608 , 0.2392157 , 0.2392157 ],
[0.28627452, 0.22352943, 0.22352943]],
[[0.28627452, 0.26666668, 0.42352945],
[0.32156864, 0.3137255 , 0.4666667 ],
[0.32941177, 0.32941177, 0.47058827],
...,
[0.30588236, 0.2509804 , 0.24705884],
[0.29803923, 0.24313727, 0.2392157 ],
[0.3137255 , 0.25882354, 0.24705884]],
...,
[[0.18039216, 0.08627451, 0.14901961],
[0.18039216, 0.08235294, 0.15686275],
[0.1764706 , 0.07843138, 0.16078432],
...,
[0.4784314 , 0.47450984, 0.4039216 ],
[0.44705886, 0.4431373 , 0.37254903],
[0.43529415, 0.43137258, 0.36078432]],
[[0.18431373, 0.07843138, 0.13725491],
[0.18039216, 0.08627451, 0.14901961],
[0.1764706 , 0.08235294, 0.14509805],
...,
[0.36862746, 0.3647059 , 0.29411766],
[0.34117648, 0.34509805, 0.27450982],
[0.34901962, 0.3529412 , 0.28235295]],
[[0.18431373, 0.08235294, 0.13333334],
[0.19215688, 0.09019608, 0.14117648],
[0.18431373, 0.09019608, 0.14509805],
...,
[0.5411765 , 0.53333336, 0.47450984],
[0.5294118 , 0.53333336, 0.47058827],
[0.5568628 , 0.56078434, 0.49803925]]]], dtype=float32),
(224, 224, 3))
Images are (224,224,3) from our rescaling, and vary from 0 to 1, from normalization
how about labels?
In [43]:
# view labels
labels
Out[43]:
array([0., 1., 1., 0., 1., 0., 1., 1., 0., 1., 0., 0., 1., 1., 1., 0., 0.,
0., 0., 1., 1., 1., 0., 0., 0., 0., 1., 1., 0., 1., 0., 1.],
dtype=float32)
Our class mode is binary, and so labels are either 0 (pizza), and 1 (steak)
Our data is now ready, and we can have the model figure the patterns between image tensors and labels.
3. Create a model (start with a basline)¶
What is our default architecture? Well, there can be many possible answers to it.
A simple trial and error to getting your start on a vision model, is using the architecture that best performs with ImageNet (a large collection of diverse images to benchmark different computer vision models).
But before that, it's good to build a smaller model get a baseline result for you to improve upon.
Note: Deep learning terms for a
small model, is less layers than (SOTA). Basically something with 3-4 layers, while ResNet50 has 50+ layers.
For our small model, we can try the one found in CNN explainer website (model_1 from above), then build a 3 layer convolutional neural network.
In [44]:
# things to import for our model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Activation
from tensorflow.keras import Sequential
In [45]:
# Create the model (can be our baseline with 3 convolutional layers)
model_4 = Sequential([
Conv2D(filters=10, kernel_size=3, strides=1, padding='valid', activation='relu', input_shape=(224,224,3)),
# filters = the number of features the model will learn
# padding = determines whether to keep or discard original spatial/image size. valid = shrink down to where kernel_size can apply its filters
Conv2D(10,3,activation='relu'),
Conv2D(10,3,activation='relu'),
Flatten(),
Dense(1, activation='sigmoid')
])
x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\layers\convolutional\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
This follows a typical CNN structure of:
Input > Conv + ReLU layers (non-linearities) > Pooling layer > Fully connected (dense layer) as Output
Let's discuss some of the components of the Conv2D layer:
- The "2D" means our inputs are two dimensional (height and width), even though they have 3 colour channels, the convolutions are run on each channel invididually.
- filters - these are the number of "feature extractors" that will be moving over our images.
- kernel_size - the size of our filters, for example, a kernel_size of (3, 3) (or just 3) will mean each filter will have the size 3x3, meaning it will look at a space of 3x3 pixels each time. The smaller the kernel, the more fine-grained features it will extract.
- stride - the number of pixels a filter will move across as it covers the image. A stride of 1 means the filter moves across each pixel 1 by 1. A stride of 2 means it moves 2 pixels at a time.
- padding - this can be either 'same' or 'valid', 'same' adds zeros the to outside of the image so the resulting output of the convolutional layer is the same as the input, where as 'valid' (default) cuts off excess pixels where the filter doesn't fit (e.g. 224 pixels wide divided by a kernel size of 3 (224/3 = 74.6) means 2 pixels will get cut off the end, as it leaves a remainder of 2.
Now let's compile the model
In [47]:
# compile the model
model_4.compile(loss='binary_crossentropy',
optimizer=Adam(),
metrics=['accuracy'])
4. Fit a model¶
It's time to fit our mode. But you may notice two extra parameters:
steps_per_epoch> this is the number of batches the model will go through. If we have a batch of 32, and steps is 10, then it will go through a total of 10 batches, aka 320 images. We want it to go through all images in ourtrain_data. (1500 images / 32 batches ~ 47 steps)validation_steps> same as above but for validation data (aka our test folder). 500 images / 32 batches ~ 16 steps.
In [48]:
# check length of training and test data generators
len(train_data), len(test_data)
Out[48]:
(47, 16)
In [49]:
# fit the model
history_4 = model_4.fit(train_data,
epochs=5,
steps_per_epoch=len(train_data),
validation_data=test_data,
validation_steps=len(test_data))
x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\trainers\data_adapters\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored. self._warn_if_super_not_called()
Epoch 1/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 28s 541ms/step - accuracy: 0.5709 - loss: 1.4794 - val_accuracy: 0.7260 - val_loss: 0.5936 Epoch 2/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 9s 197ms/step - accuracy: 0.7388 - loss: 0.5303 - val_accuracy: 0.8020 - val_loss: 0.4486 Epoch 3/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 10s 205ms/step - accuracy: 0.8686 - loss: 0.3660 - val_accuracy: 0.7660 - val_loss: 0.4861 Epoch 4/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 10s 214ms/step - accuracy: 0.9386 - loss: 0.1896 - val_accuracy: 0.8340 - val_loss: 0.4227 Epoch 5/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 10s 215ms/step - accuracy: 0.9898 - loss: 0.0587 - val_accuracy: 0.8460 - val_loss: 0.4319
5. Evaluate the model¶
Oh yeah! Looks like our model is learning something!
Let's check the training curves
In [52]:
!pip install pandas
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In [54]:
# plot the training curves
import pandas as pd
pd.DataFrame(history_4.history).plot(figsize=(10,7));
accuracy almost seems like it's reach 100% accuracy after 5 epochs. But val_accuracy has some struggles maintaining accuracy. There's a possibility the model is overfitting.
Let's separate the loss curves from accuracy to get a clearer picture
In [64]:
# plot the loss and accuracy data separately
def plot_loss_curves(history):
"""
Returns separate loss curves for training and validation metrics.
"""
loss = history.history['loss']
val_loss = history.history['val_loss']
accuracy = history.history['accuracy']
val_accuracy = history.history['val_accuracy']
epochs = range(len(history.history['loss']))
# plot loss
plt.figure()
plt.plot(epochs, loss, label='Training Loss')
plt.plot(epochs, val_loss, label='Validation Loss')
plt.title('Loss')
plt.xlabel('Epochs')
plt.legend();
# plot accuracy
plt.figure()
plt.plot(epochs, accuracy, label='Training Accuracy')
plt.plot(epochs, val_accuracy, label='Validation Accuracy')
plt.title('Accuracy')
plt.xlabel('Epochs')
plt.legend();
In [65]:
# check out loss and accuracy curves in model_4
plot_loss_curves(history_4)
The ideal position of validation, is trailing behind training in terms of accuracy and loss. If you notice that it's growing a big gap overtime, its likely overfitting.
In [66]:
# check model's architecture
model_4.summary()
Model: "sequential_4"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ conv2d_4 (Conv2D) │ (None, 222, 222, 10) │ 280 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_5 (Conv2D) │ (None, 220, 220, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_6 (Conv2D) │ (None, 218, 218, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ flatten_4 (Flatten) │ (None, 475240) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_10 (Dense) │ (None, 1) │ 475,241 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 1,432,025 (5.46 MB)
Trainable params: 477,341 (1.82 MB)
Non-trainable params: 0 (0.00 B)
Optimizer params: 954,684 (3.64 MB)
6. Adjust the model parameters¶
There's 3 steps to fitting a ML model:
- Create a baseline
- Beat the baseline by overfitting a larger model
- Reduce overfitting
We've done steps 1 and 2, but there are other ways to continue overfitting:
- Increase the number of convoluional layers
- Increase the number of convolutional filters
- Add another dense layer to the output of our flattened layer
Our focus now, is to get the training and validation training curves closer together, aka reduce overfitting.
But why care about overfitting if training accuracy is so good? If a model performs so well at training data, but not so well in validation, the model has a porr ability at prediction with unseen data. It's not able to generalize and learn about the real world patterns, but instead learning patterns that aren't supposed to be patterns in the training data.
So for the next few models we build, we'll adjust the number of parameters and inspect the training curves as well.
We'll build 2 models:
- A CNN with max pooling
- A CNN with mac pooling and data augmentation
For the first model, we'll follow this CNN structure:
Input > Conv layers + ReLU layers > Max pooling layers > Fully connected (Dense) layers as output
This model will have the same structure as model_4, but with Max Pooling layer included
In [67]:
# create the model (a 3 layer convolutional neural network)
model_5 = Sequential([
Conv2D(10,3,activation='relu',input_shape=(224,224,3)),
MaxPool2D(pool_size=2), # reduces number of features by half
Conv2D(10,3,activation='relu'),
MaxPool2D(), # doing this for every CNN layer
Conv2D(10,3,activation='relu'),
MaxPool2D(),
Flatten(),
Dense(1,activation='sigmoid')
])
x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\layers\convolutional\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
In [68]:
# compile the model
model_5.compile(loss='binary_crossentropy',
optimizer=Adam(),
metrics=['accuracy'])
In [70]:
# fit the model
history_5 = model_5.fit(train_data,
epochs=5,
steps_per_epoch=len(train_data),
validation_data=test_data,
validation_steps=len(test_data))
Epoch 1/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 25s 491ms/step - accuracy: 0.6468 - loss: 0.6387 - val_accuracy: 0.8000 - val_loss: 0.4548 Epoch 2/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 6s 130ms/step - accuracy: 0.7494 - loss: 0.5103 - val_accuracy: 0.8000 - val_loss: 0.4095 Epoch 3/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 6s 132ms/step - accuracy: 0.8167 - loss: 0.4251 - val_accuracy: 0.8260 - val_loss: 0.3699 Epoch 4/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 6s 132ms/step - accuracy: 0.8250 - loss: 0.3963 - val_accuracy: 0.8360 - val_loss: 0.3585 Epoch 5/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 6s 132ms/step - accuracy: 0.8144 - loss: 0.3979 - val_accuracy: 0.8740 - val_loss: 0.3554
It seems model_5 is performing worse on training data, but is better on validation data instead.
Before checking training curves, let's see the architecture
In [71]:
# check model architecture
model_5.summary()
Model: "sequential_5"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ conv2d_7 (Conv2D) │ (None, 222, 222, 10) │ 280 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_2 (MaxPooling2D) │ (None, 111, 111, 10) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_8 (Conv2D) │ (None, 109, 109, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_3 (MaxPooling2D) │ (None, 54, 54, 10) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_9 (Conv2D) │ (None, 52, 52, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_4 (MaxPooling2D) │ (None, 26, 26, 10) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ flatten_5 (Flatten) │ (None, 6760) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_11 (Dense) │ (None, 1) │ 6,761 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 26,585 (103.85 KB)
Trainable params: 8,861 (34.61 KB)
Non-trainable params: 0 (0.00 B)
Optimizer params: 17,724 (69.24 KB)
Notice how MaxPooling2D is halving the output shape everytime it's applied, while trying to extract the most important data within the 2x2 square before shrinking down.
The bigger the pool_size, the more that max pooling will squeeze the features out of the image. But if too big, there wouldn't be enough features to keep, thus learning nothing.
It's a major reduction in trainable parameters. To 8,861 from 477,431 in model_4
Time to check loss curves
In [72]:
# plot loss curves of model_5 results
plot_loss_curves(history_5)
We can see the curves are closer to each other, but the validation loss looks like it's flattening out, risking the potential of overfitting again.
Time to try other overfitting prevention, and also data augmentation.
We will first observe how it's done with code, then explain it's guts. For data aufmentation, we'll need to reinstate ImageDataGenerator instance.
In [75]:
# create ImageDataGenerator training instance with data augmentation
train_datagen_augmentation = ImageDataGenerator(rescale=1/255.,
rotation_range=20, # rotate the image between 0 and 20 degrees,
shear_range=0.2, # shear the image,
zoom_range=0.2, # zoom into the image,
width_shift_range=0.2, # shift the image width ways,
height_shift_range=0.2, # shift the image height ways,
horizontal_flip=True) # flipping image in horizontal axis
# create imagedatagenerator training instance without data augmentation
train_datagen = ImageDataGenerator(rescale=1/255.)
# create imagedatagenerator test instance without data augmentation
test_datagen = ImageDataGenerator(rescale=1/255.)
Now, what's data augmentation?
It's the process of altering our training data by a slight margin. This gives more diversity in training data, and allows the model to learn more generalizable patterns. Whether that be rotating the image slightly, flipping image horizontally, or slight cropping of the edges.
This helps simulate the data a model may encouter in the real world.
Note: Data augmentation is only ever performed on training data.
ImageDataGeneratordoes augmentation on random lots of images, but leave them that way once training commences. If done on validation/test, we wouldn't be able to properly compare, as random images are changed, every time it's being validated.
In [82]:
# import data and augment it from training directory
print('Augmented training images:')
train_data_augmented = train_datagen_augmentation.flow_from_directory(train_dir,
target_size=(224,224),
batch_size=32,
class_mode='binary',
shuffle=False) # keeping this for demonstration purposes. But it's good to shuffle
# create non-augmented data batches
print('Non-augmented training images:')
train_data = train_datagen.flow_from_directory(train_dir,
target_size=(224,224),
batch_size=32,
class_mode='binary',
shuffle=False)
print('Unchanged test images:')
test_data = test_datagen.flow_from_directory(test_dir,
target_size=(224,224),
batch_size=32,
class_mode='binary',
shuffle='binary')
Augmented training images: Found 1500 images belonging to 2 classes. Non-augmented training images: Found 1500 images belonging to 2 classes. Unchanged test images: Found 500 images belonging to 2 classes.
Let's visualize the augmented, and non augmented data
In [84]:
!pip install scipy
Collecting scipy Downloading scipy-1.15.3-cp310-cp310-win_amd64.whl.metadata (60 kB) Requirement already satisfied: numpy<2.5,>=1.23.5 in x:\anaconda3\envs\tf_gpu\lib\site-packages (from scipy) (2.1.3) Downloading scipy-1.15.3-cp310-cp310-win_amd64.whl (41.3 MB) ---------------------------------------- 0.0/41.3 MB ? eta -:--:-- -- ------------------------------------- 2.6/41.3 MB 12.5 MB/s eta 0:00:04 ----- ---------------------------------- 5.8/41.3 MB 13.6 MB/s eta 0:00:03 -------- ------------------------------- 8.4/41.3 MB 13.3 MB/s eta 0:00:03 ---------- ----------------------------- 11.0/41.3 MB 13.0 MB/s eta 0:00:03 ------------- -------------------------- 13.9/41.3 MB 13.2 MB/s eta 0:00:03 ---------------- ----------------------- 17.0/41.3 MB 13.4 MB/s eta 0:00:02 ------------------- -------------------- 20.2/41.3 MB 13.6 MB/s eta 0:00:02 ---------------------- ----------------- 23.1/41.3 MB 13.5 MB/s eta 0:00:02 ------------------------- -------------- 26.0/41.3 MB 13.6 MB/s eta 0:00:02 --------------------------- ------------ 28.0/41.3 MB 13.2 MB/s eta 0:00:02 ----------------------------- ---------- 30.7/41.3 MB 13.1 MB/s eta 0:00:01 -------------------------------- ------- 33.6/41.3 MB 13.2 MB/s eta 0:00:01 ----------------------------------- ---- 36.4/41.3 MB 13.2 MB/s eta 0:00:01 ------------------------------------- -- 39.1/41.3 MB 13.1 MB/s eta 0:00:01 --------------------------------------- 41.2/41.3 MB 13.0 MB/s eta 0:00:01 ---------------------------------------- 41.3/41.3 MB 12.3 MB/s eta 0:00:00 Installing collected packages: scipy Successfully installed scipy-1.15.3
In [85]:
# get data batch samples
images, labels = next(train_data)
augmented_images, augmented_labels = next(train_data_augmented) # labels aren't augmented by the way
In [86]:
# show original image and augmented image
random_number = random.randint(0,31) # since we're pulling a batch of 32, we'll be choosing between the index a random picture
plt.imshow(images[random_number])
plt.title(f'Original image')
plt.axis(False)
plt.figure()
plt.imshow(augmented_images[random_number])
plt.title(f'Augmented image')
plt.axis(False);
You can see the slight difference between original, and augmented. With the slight warping and cutting and rotating, this forces the model to learn patterns on less ideal images. Which is often the case with real world images.
Data augmentation is a great solution if you find that your model is overfitting too much. As for how strong augmentation should be, there's no set rule for it. It'd be best to check the options in ImageDataGenerator class, and think about your particular use case with a model and its data.
Now let's try refit the model on the augmented images for model_5
In [90]:
# create the model (same as model_5)
model_6 = Sequential([
Conv2D(10,3,activation='relu',input_shape=(224,224,3)),
MaxPool2D(pool_size=2),
Conv2D(10,3,activation='relu'),
MaxPool2D(),
Conv2D(10,3,activation='relu'),
MaxPool2D(),
Flatten(),
Dense(1,activation='sigmoid')
])
# compile model
model_6.compile(loss='binary_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
# fit the model
history_6 = model_6.fit(train_data_augmented,
epochs=5,
steps_per_epoch=len(train_data_augmented),
validation_data=test_data,
validation_steps=len(test_data))
x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\layers\convolutional\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Epoch 1/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 20s 404ms/step - accuracy: 0.5125 - loss: 0.8797 - val_accuracy: 0.4920 - val_loss: 0.6938 Epoch 2/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 18s 390ms/step - accuracy: 0.4448 - loss: 0.6950 - val_accuracy: 0.5240 - val_loss: 0.6920 Epoch 3/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 18s 382ms/step - accuracy: 0.5127 - loss: 0.6914 - val_accuracy: 0.5080 - val_loss: 0.6865 Epoch 4/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 18s 381ms/step - accuracy: 0.5620 - loss: 0.6829 - val_accuracy: 0.6200 - val_loss: 0.6837 Epoch 5/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 18s 390ms/step - accuracy: 0.5723 - loss: 0.6834 - val_accuracy: 0.5460 - val_loss: 0.6752
It appears the model didn't get as good of results this time. Why?
It's because we turned off data_shuffling with shuffle=False, which means the model sees the same batches of image over and over.
Our data is organized by folders, and it draws out only the pizza folder first. With it's batches, it only has pizzas to compare to. Not steak. Shuffle helps solve this issue by having that mix of pizza and steak in every batch.
Now knowing what's wrong, we can flip to shuffle=True, as we're done with demonstration purposes.
You can see how data augmentation also increases training time as well. But there's an option to possibly speed up with TensorFlow's parallel reads and buffered prefecting options.
In [91]:
# check model performance
plot_loss_curves(history_6)
Our accuracy is jumping quite a lot. It's heading into the right direction, but we want to aim for idealy a smooth decreasing ascent that reaches close to 1.
Now let's try the suffled augmented data
In [92]:
# import data and augment it from directories
train_data_augmented_shuffled = train_datagen_augmentation.flow_from_directory(train_dir,
target_size=(224,224),
batch_size=32,
class_mode='binary',
shuffle=True)
Found 1500 images belonging to 2 classes.
In [95]:
# create model, same as 5 and 6
model_7 = Sequential([
Conv2D(10,3,activation='relu',input_shape=(224,224,3)),
MaxPool2D(pool_size=2),
Conv2D(10,3,activation='relu'),
MaxPool2D(),
Conv2D(10,3,activation='relu'),
MaxPool2D(),
Flatten(),
Dense(1,activation='sigmoid')
])
# compile model
model_7.compile(loss='binary_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
# fit the model
history_7 = model_7.fit(train_data_augmented_shuffled,
epochs=5,
steps_per_epoch=len(train_data_augmented_shuffled),
validation_data=test_data,
validation_steps=len(test_data))
x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\layers\convolutional\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs) x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\trainers\data_adapters\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored. self._warn_if_super_not_called()
Epoch 1/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 18s 367ms/step - accuracy: 0.5350 - loss: 0.7032 - val_accuracy: 0.7380 - val_loss: 0.5723 Epoch 2/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 17s 368ms/step - accuracy: 0.7090 - loss: 0.5906 - val_accuracy: 0.8240 - val_loss: 0.4201 Epoch 3/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 17s 363ms/step - accuracy: 0.7435 - loss: 0.5351 - val_accuracy: 0.8160 - val_loss: 0.3851 Epoch 4/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 17s 362ms/step - accuracy: 0.7734 - loss: 0.4889 - val_accuracy: 0.8280 - val_loss: 0.3554 Epoch 5/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 17s 358ms/step - accuracy: 0.7625 - loss: 0.5001 - val_accuracy: 0.8540 - val_loss: 0.3528
In [96]:
# check model's performance history training on augmented data
plot_loss_curves(history_7)
model_7 has shown how it has improved consistently compared to model_6, due to the shuffling done.
Also our loss curve does appear to be more smoother as well.
7. Repeat until satisfied¶
We've beaten the basline quite well, and there are more ways to continue to improve model:
- Increase the model layers (e.g. more CNN layers)
- Increase the filter numbers in each conv layers (e.g. from 10 to 32, 64, to 128. These are usual values for trial and error)
- Train for longer (more epochs)
- Finding an ideal learning rate
- Get more data
- Use transfer learning to leverage what other image models has learnt and adjust for our specific case
Adjusting these settings (except last 2) during development is known as hyperparameter tuning.
Let's go back to model_1 with the TinyVGG architecture
In [97]:
# Create a CNN model (same as Tiny VGG but for binary classification)
model_8 = Sequential([
Conv2D(10,3,activation='relu',input_shape=(224,224,3)),
Conv2D(10,3,activation='relu'),
MaxPool2D(),
Conv2D(10,3,activation='relu'),
Conv2D(10,3,activation='relu'),
MaxPool2D(),
Flatten(),
Dense(1, activation='sigmoid')
])
# compile the model
model_8.compile(loss='binary_crossentropy',
optimizer=tf.keras.optimizers.Adam(),
metrics=['accuracy'])
# fit the model
history_8 = model_8.fit(train_data_augmented_shuffled,
epochs=5,
steps_per_epoch=len(train_data_augmented_shuffled),
validation_data=test_data,
validation_steps=len(test_data))
x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\layers\convolutional\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Epoch 1/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 22s 422ms/step - accuracy: 0.6118 - loss: 0.6556 - val_accuracy: 0.8280 - val_loss: 0.4363 Epoch 2/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 20s 423ms/step - accuracy: 0.7695 - loss: 0.4911 - val_accuracy: 0.8680 - val_loss: 0.3613 Epoch 3/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 20s 419ms/step - accuracy: 0.7753 - loss: 0.4775 - val_accuracy: 0.8860 - val_loss: 0.3220 Epoch 4/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 20s 416ms/step - accuracy: 0.7944 - loss: 0.4477 - val_accuracy: 0.8620 - val_loss: 0.3247 Epoch 5/5 47/47 ━━━━━━━━━━━━━━━━━━━━ 20s 423ms/step - accuracy: 0.8036 - loss: 0.4459 - val_accuracy: 0.8880 - val_loss: 0.2907
Note: You may notice some difference between
model_8andmodel_1, mostly code fromtensorflow.keras.layerstoimport Conv2D. It reduces the amount of code, but does the same thing, calling the same thing.
In [98]:
# check architecture of model_1
model_1.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 222, 222, 10) │ 280 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_1 (Conv2D) │ (None, 220, 220, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 110, 110, 10) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_2 (Conv2D) │ (None, 108, 108, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_3 (Conv2D) │ (None, 106, 106, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 53, 53, 10) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ flatten (Flatten) │ (None, 28090) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ (None, 1) │ 28,091 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 93,305 (364.48 KB)
Trainable params: 31,101 (121.49 KB)
Non-trainable params: 0 (0.00 B)
Optimizer params: 62,204 (242.99 KB)
In [99]:
# check architecture of model_8
model_8.summary()
Model: "sequential_11"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ conv2d_25 (Conv2D) │ (None, 222, 222, 10) │ 280 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_26 (Conv2D) │ (None, 220, 220, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_20 (MaxPooling2D) │ (None, 110, 110, 10) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_27 (Conv2D) │ (None, 108, 108, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_28 (Conv2D) │ (None, 106, 106, 10) │ 910 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_21 (MaxPooling2D) │ (None, 53, 53, 10) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ flatten_11 (Flatten) │ (None, 28090) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_17 (Dense) │ (None, 1) │ 28,091 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 93,305 (364.48 KB)
Trainable params: 31,101 (121.49 KB)
Non-trainable params: 0 (0.00 B)
Optimizer params: 62,204 (242.99 KB)
Now let's check our TinyVGG model's performance
In [100]:
# check out the TinyVGG model performance
plot_loss_curves(history_8)
In [103]:
# visually compare with model_1's history
plot_loss_curves(history_1)
Training curve looks good, though improvement isn't that much impressive compared to the previous model. Maybe it's time to give the model more training time, aka more epochs.
Making a prediction with our trained model¶
What good is a trained model, if you can't make predictions with it? Let's upload a couple of our own images to see how the model reacts with it.
In [104]:
# classes we're working with
print(class_names)
['pizza' 'steak']
We'll use this steak image to do our first test
In [106]:
# view our example image
steak=mpimg.imread('03-steak.jpg')
plt.imshow(steak)
plt.axis(False);
In [107]:
# check the shape of our image
steak.shape
Out[107]:
(4032, 3024, 3)
Our model only takes the shape of (224, 224, 3), and thus we need to reshape our custom image, to fit the inputs for the model.
We'll need to decode the image with tf.io.read_file (for reading files) and tf.image (for resizing image/turning into tensor).
In [108]:
# create a function to import an image and resize it, so it can be used in model
def load_and_prep_image(filename, img_shape=224):
"""
Reads an image from filename, turns it into a tensor and reshapes it to (img_shape, img_shape, colour channel)
"""
# read in target file (on image)
img = tf.io.read_file(filename)
# decode read file into tensor, and confirm it still has 3 colour channels
# (out model is trained with 3 colour channels, but some images may have 4)
img = tf.image.decode_image(img, channels=3)
# resize image to the size the model has been trained on
img = tf.image.resize(img, size=[img_shape,img_shape])
# rescale image so all values are within 0 and 1
img = img/255.
return img
We now have a function to load custom images for our model. Time to load in the image
In [109]:
# load in and preprocess custom image
steak = load_and_prep_image('03-steak.jpg')
steak
Out[109]:
<tf.Tensor: shape=(224, 224, 3), dtype=float32, numpy=
array([[[0.6377451 , 0.6220588 , 0.57892156],
[0.6504902 , 0.63186276, 0.5897059 ],
[0.63186276, 0.60833335, 0.5612745 ],
...,
[0.52156866, 0.05098039, 0.09019608],
[0.49509802, 0.04215686, 0.07058824],
[0.52843136, 0.07745098, 0.10490196]],
[[0.6617647 , 0.6460784 , 0.6107843 ],
[0.6387255 , 0.6230392 , 0.57598037],
[0.65588236, 0.63235295, 0.5852941 ],
...,
[0.5352941 , 0.06862745, 0.09215686],
[0.529902 , 0.05931373, 0.09460784],
[0.5142157 , 0.05539216, 0.08676471]],
[[0.6519608 , 0.6362745 , 0.5892157 ],
[0.6392157 , 0.6137255 , 0.56764704],
[0.65637255, 0.6269608 , 0.5828431 ],
...,
[0.53137255, 0.06470589, 0.08039216],
[0.527451 , 0.06862745, 0.1 ],
[0.52254903, 0.05196078, 0.0872549 ]],
...,
[[0.49313724, 0.42745098, 0.31029412],
[0.05441177, 0.01911765, 0. ],
[0.2127451 , 0.16176471, 0.09509804],
...,
[0.6132353 , 0.59362745, 0.57009804],
[0.65294117, 0.6333333 , 0.6098039 ],
[0.64166665, 0.62990195, 0.59460783]],
[[0.65392154, 0.5715686 , 0.45 ],
[0.6367647 , 0.54656863, 0.425 ],
[0.04656863, 0.01372549, 0. ],
...,
[0.6372549 , 0.61764705, 0.59411764],
[0.63529414, 0.6215686 , 0.5892157 ],
[0.6401961 , 0.62058824, 0.59705883]],
[[0.1 , 0.05539216, 0. ],
[0.48333332, 0.40882352, 0.29117647],
[0.65 , 0.5686275 , 0.44019607],
...,
[0.6308824 , 0.6161765 , 0.5808824 ],
[0.6519608 , 0.63186276, 0.5901961 ],
[0.6338235 , 0.6259804 , 0.57892156]]], dtype=float32)>
Nice, let's test it with the model!
In [111]:
model_8.predict(steak)
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[111], line 1 ----> 1 model_8.predict(steak) File x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\utils\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs) 119 filtered_tb = _process_traceback_frames(e.__traceback__) 120 # To get the full stack trace, call: 121 # `keras.config.disable_traceback_filtering()` --> 122 raise e.with_traceback(filtered_tb) from None 123 finally: 124 del filtered_tb File x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\models\functional.py:276, in Functional._adjust_input_rank(self, flat_inputs) 274 adjusted.append(ops.expand_dims(x, axis=-1)) 275 continue --> 276 raise ValueError( 277 f"Invalid input shape for input {x}. Expected shape " 278 f"{ref_shape}, but input has incompatible shape {x.shape}" 279 ) 280 # Add back metadata. 281 for i in range(len(flat_inputs)): ValueError: Exception encountered when calling Sequential.call(). Invalid input shape for input Tensor("data:0", shape=(32, 224, 3), dtype=float32). Expected shape (None, 224, 224, 3), but input has incompatible shape (32, 224, 3) Arguments received by Sequential.call(): • inputs=tf.Tensor(shape=(32, 224, 3), dtype=float32) • training=False • mask=None • kwargs=<class 'inspect._empty'>
Somethings wrong... The image is in the same shape for what the model expects, but there's an extra dimension missing.
This is likely from the batch size dimension > (batch_size, 224, 224, 3).
We can fix this by adding extra dims with tf.expand_dims.
In [112]:
# add an extra axis
print(f'Shape before new dimension: {steak.shape}')
steak = tf.expand_dims(steak, axis=0) # add an extra dimension at axis 0
# steak = steak[tf.newaxis, ...] is the alternative for the above
print(f'Shape after new dimension: {steak.shape}')
steak
Shape before new dimension: (224, 224, 3) Shape after new dimension: (1, 224, 224, 3)
Out[112]:
<tf.Tensor: shape=(1, 224, 224, 3), dtype=float32, numpy=
array([[[[0.6377451 , 0.6220588 , 0.57892156],
[0.6504902 , 0.63186276, 0.5897059 ],
[0.63186276, 0.60833335, 0.5612745 ],
...,
[0.52156866, 0.05098039, 0.09019608],
[0.49509802, 0.04215686, 0.07058824],
[0.52843136, 0.07745098, 0.10490196]],
[[0.6617647 , 0.6460784 , 0.6107843 ],
[0.6387255 , 0.6230392 , 0.57598037],
[0.65588236, 0.63235295, 0.5852941 ],
...,
[0.5352941 , 0.06862745, 0.09215686],
[0.529902 , 0.05931373, 0.09460784],
[0.5142157 , 0.05539216, 0.08676471]],
[[0.6519608 , 0.6362745 , 0.5892157 ],
[0.6392157 , 0.6137255 , 0.56764704],
[0.65637255, 0.6269608 , 0.5828431 ],
...,
[0.53137255, 0.06470589, 0.08039216],
[0.527451 , 0.06862745, 0.1 ],
[0.52254903, 0.05196078, 0.0872549 ]],
...,
[[0.49313724, 0.42745098, 0.31029412],
[0.05441177, 0.01911765, 0. ],
[0.2127451 , 0.16176471, 0.09509804],
...,
[0.6132353 , 0.59362745, 0.57009804],
[0.65294117, 0.6333333 , 0.6098039 ],
[0.64166665, 0.62990195, 0.59460783]],
[[0.65392154, 0.5715686 , 0.45 ],
[0.6367647 , 0.54656863, 0.425 ],
[0.04656863, 0.01372549, 0. ],
...,
[0.6372549 , 0.61764705, 0.59411764],
[0.63529414, 0.6215686 , 0.5892157 ],
[0.6401961 , 0.62058824, 0.59705883]],
[[0.1 , 0.05539216, 0. ],
[0.48333332, 0.40882352, 0.29117647],
[0.65 , 0.5686275 , 0.44019607],
...,
[0.6308824 , 0.6161765 , 0.5808824 ],
[0.6519608 , 0.63186276, 0.5901961 ],
[0.6338235 , 0.6259804 , 0.57892156]]]], dtype=float32)>
In [113]:
# make a prediction on custom image
pred = model_8.predict(steak)
pred
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 175ms/step
Out[113]:
array([[0.8684933]], dtype=float32)
Our prediction comes out as a probability between pizza and steak. Knowing it's binary classification, 0.5 is the middle between steak and pizza prediction. So with a value of 0.86, is most likely going to be the positive class of 1.
But positive or negative class don't give much clue as to whether it's pizza or steak. So we should write a function to convert the prediction probabilities to the class names.
In [114]:
# we can index the predicted class by rounding the probability
pred_class = class_names[int(tf.round(pred)[0][0])] # the [0][0] selects the value from the 2D tensor, after rounding > [[1.0]] is what the number actually looks like when rounded
pred_class
Out[114]:
np.str_('steak')
In [118]:
def pred_and_plot(model, filename, class_names):
"""
Imports an image located at filename, makes prediction with model, and plots image with predicted class as title
"""
# import the target image/preprocess it
img = load_and_prep_image(filename)
# make a prediction
pred = model.predict(tf.expand_dims(img, axis=0))
# get the predicted value
pred_class = class_names[int(tf.round(pred)[0][0])]
# plot the image and predicted label
plt.imshow(img)
plt.title(f"Prediction: {pred_class}")
plt.axis(False);
In [119]:
# test our model with custom image
pred_and_plot(model_8, '03-steak.jpg', class_names)
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 83ms/step
Nice, the model got it correct!
In [123]:
# download another image to make a prediction
pred_and_plot(model_8, '03-pizza-dad.jpeg', class_names)
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
Multi-class classification¶
We've referenced TinyVGG architecture from the CNN explainer website. But the CNN explainer worked with 10 categories, over binary classification like ours.
Let's go through the same steps again, but this time, we'll work with 10 different categories of food.
The workflow we're doing is a slightly modified version of the above. As you do more deep learning, the above workflow becomes more like an outline, rather than a step-by-step guide
1. Import and become one with the data¶
Again going back to the Food101 dataset, in addition to the steak and pizza, we'll pull out 8 other categories to satisfy our next challenge.
In [124]:
import zipfile
import urllib.request
# Step 1: Download the zip file 10_food_classes
url = "https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip"
urllib.request.urlretrieve(url, "10_food_classes_all_data.zip") # saves the file locally
# Step 2: Unzip the file
with zipfile.ZipFile("10_food_classes_all_data.zip", "r") as zip_ref:
zip_ref.extractall() # extract all files to the current directory
Now let's check out all the different directories/sub-directories in the 10_food_classes file.
In [126]:
import os
# walk through 10_food_classes directory and list number of files
for dirpath, dirnames, filenames in os.walk('10_food_classes_all_data'):
print(f'There are {len(dirnames)} directories and {len(filenames)} images in "{dirpath}".')
There are 2 directories and 0 images in "10_food_classes_all_data". There are 10 directories and 0 images in "10_food_classes_all_data\test". There are 0 directories and 250 images in "10_food_classes_all_data\test\ice_cream". There are 0 directories and 250 images in "10_food_classes_all_data\test\chicken_curry". There are 0 directories and 250 images in "10_food_classes_all_data\test\steak". There are 0 directories and 250 images in "10_food_classes_all_data\test\sushi". There are 0 directories and 250 images in "10_food_classes_all_data\test\chicken_wings". There are 0 directories and 250 images in "10_food_classes_all_data\test\grilled_salmon". There are 0 directories and 250 images in "10_food_classes_all_data\test\hamburger". There are 0 directories and 250 images in "10_food_classes_all_data\test\pizza". There are 0 directories and 250 images in "10_food_classes_all_data\test\ramen". There are 0 directories and 250 images in "10_food_classes_all_data\test\fried_rice". There are 10 directories and 0 images in "10_food_classes_all_data\train". There are 0 directories and 750 images in "10_food_classes_all_data\train\ice_cream". There are 0 directories and 750 images in "10_food_classes_all_data\train\chicken_curry". There are 0 directories and 750 images in "10_food_classes_all_data\train\steak". There are 0 directories and 750 images in "10_food_classes_all_data\train\sushi". There are 0 directories and 750 images in "10_food_classes_all_data\train\chicken_wings". There are 0 directories and 750 images in "10_food_classes_all_data\train\grilled_salmon". There are 0 directories and 750 images in "10_food_classes_all_data\train\hamburger". There are 0 directories and 750 images in "10_food_classes_all_data\train\pizza". There are 0 directories and 750 images in "10_food_classes_all_data\train\ramen". There are 0 directories and 750 images in "10_food_classes_all_data\train\fried_rice".
Looks good! Now we set up train and test directory path.
In [127]:
train_dir = '10_food_classes_all_data/train/'
test_dir = '10_food_classes_all_data/test/'
Get the class names from subdirectories
In [128]:
# get the class names for our multi-class dataset
import pathlib
import numpy as np
data_dir = pathlib.Path(train_dir)
class_names = np.array(sorted([item.name for item in data_dir.glob('*')])) # * > matching everything inside the directory
print(class_names)
['chicken_curry' 'chicken_wings' 'fried_rice' 'grilled_salmon' 'hamburger' 'ice_cream' 'pizza' 'ramen' 'steak' 'sushi']
In [136]:
# view a random image in the file
import random
img = view_random_image(target_dir=train_dir, target_class=random.choice(class_names)) # gets a random class name
Image shape: (512, 382, 3)
2. Preprocess the data (prepare it for a model)¶
After going through some images (10-100), looks like everything is set up correctly.
Time to preprocess the data
In [139]:
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# rescale the data and create data generator instances
train_datagen = ImageDataGenerator(rescale=1/255.)
test_datagen = ImageDataGenerator(rescale=1/255.)
# load data into directory, and turn them into batches
train_data = train_datagen.flow_from_directory(train_dir,
target_size=(224,224),
batch_size=32,
class_mode='categorical')
test_data = test_datagen.flow_from_directory(test_dir,
target_size=(224,224),
batch_size=32,
class_mode='categorical')
Found 7500 images belonging to 10 classes. Found 2500 images belonging to 10 classes.
The main change from our binary classifier, to 10 categories, is changing class_mode from class_mode to categorical. Everything else stays the same.
Question: But why do we set our image as 224x224? It's often a very common default size for preprocessing images. But depends on your problem, whether a bigger or smaller image is needed.
3. Create a model (start with a baseline)¶
We can use the same model (TinyVGG) we've used for binary classification problem, for our multi-class classification problem with a couple of small tweaks.
Namely:
- Changing the output to have 10 output neurons (the same number of categories).
- Changing the output layer to use
softmaxoversigmoid. - Changing the loss function to be
categorical_crossentropyoverbinary_crossentropy.
In [145]:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense
# create our model (clone of model_8, but for classification)
model_9 = Sequential([
Conv2D(10,3,activation='relu',input_shape=(224,224,3)),
Conv2D(10,3,activation='relu'),
MaxPool2D(),
Conv2D(10,3,activation='relu'),
Conv2D(10,3,activation='relu'),
MaxPool2D(),
Flatten(),
Dense(10,activation='softmax')
])
# compile the model
model_9.compile(loss='categorical_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
4. Fit a model¶
Now we've got a model fit for multi classes. Let's start fitting it
In [146]:
# fit the model
history_9 = model_9.fit(train_data,
epochs=5,
steps_per_epoch=len(train_data),
validation_data=test_data,
validation_steps=len(test_data))
Epoch 1/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 84s 351ms/step - accuracy: 0.1707 - loss: 2.2480 - val_accuracy: 0.2756 - val_loss: 1.9998 Epoch 2/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 48s 205ms/step - accuracy: 0.3320 - loss: 1.9258 - val_accuracy: 0.2648 - val_loss: 2.0030 Epoch 3/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 44s 186ms/step - accuracy: 0.4773 - loss: 1.5691 - val_accuracy: 0.2796 - val_loss: 2.0894 Epoch 4/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 43s 182ms/step - accuracy: 0.7336 - loss: 0.8674 - val_accuracy: 0.2568 - val_loss: 2.6161 Epoch 5/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 43s 181ms/step - accuracy: 0.9222 - loss: 0.3081 - val_accuracy: 0.2904 - val_loss: 3.7356
It's noticeably longer to train the model, despite the same epoch of our binary class model. This is due to the amount of images the model has to process through compared to binary. Train has 750 images, and test has 250 images. Our binary had 2 classes, but this one has 10 classes. Our model above is dealing with 5 times as much data as the binary.
5. Evaluate the model¶
Yay we've trained the model :) Let's see it visually
In [147]:
# evaluate on test data
model_9.evaluate(test_data)
79/79 ━━━━━━━━━━━━━━━━━━━━ 8s 95ms/step - accuracy: 0.3031 - loss: 3.6326
Out[147]:
[3.735577344894409, 0.2903999984264374]
In [148]:
# check model curves
plot_loss_curves(history_9)
Hmm, pretty poor results on our training and validation loss curves. So what does this say?
It looks like the model has overfit our training accuracy, and is poorly generalizing/predicting on data it's unfamiliar with.
6. Adjust the model parameters¶
It's clear that the model is learning something, but it's not in the intended direction. We ideally want our validation to perform as well as training. So our next steps is to try and prevent overfitting from occuring.
- Get more data - Simplest but hardest answer. It gives more opportunity for the model to learn patterns
- Simplify model - If the model overfits, it can mean the model is too complicated. It's learning patterns too well, or learning patterns that aren't really patterns. Which makes it hard to generalize on unseen data. We can reduce layers or the number of hidden units.
- Use data augmentation - Manipulating the training data slightly, which makes learning harder/adds more variety in data. If the model can learn from augmented data, then it may be able to generalize better on unseen data.
- Use transfer learning - We can leverage an already trained model, who's recognized patterns in similar data, into the foundation of our own task. Such as using a computer vision model, and tweak it slightly to suit our food data.
For now, lets simplify the model first. We'll remove two conv layer, so our layers go from four to two.
In [149]:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense
# create our model (clone of model_8, but for classification)
model_10 = Sequential([
Conv2D(10,3,activation='relu',input_shape=(224,224,3)),
MaxPool2D(),
Conv2D(10,3,activation='relu'),
MaxPool2D(),
Flatten(),
Dense(10,activation='softmax')
])
# compile the model
model_10.compile(loss='categorical_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
# fit the model
history_10 = model_10.fit(train_data,
epochs=5,
steps_per_epoch=len(train_data),
validation_data=test_data,
validation_steps=len(test_data))
x:\Anaconda3\envs\tf_gpu\lib\site-packages\keras\src\layers\convolutional\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Epoch 1/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 39s 160ms/step - accuracy: 0.1917 - loss: 2.1982 - val_accuracy: 0.2984 - val_loss: 1.9639 Epoch 2/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 35s 150ms/step - accuracy: 0.4483 - loss: 1.6929 - val_accuracy: 0.3288 - val_loss: 1.9280 Epoch 3/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 36s 152ms/step - accuracy: 0.6271 - loss: 1.2168 - val_accuracy: 0.2868 - val_loss: 2.1241 Epoch 4/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 37s 158ms/step - accuracy: 0.7917 - loss: 0.7238 - val_accuracy: 0.2924 - val_loss: 2.4491 Epoch 5/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 37s 157ms/step - accuracy: 0.9233 - loss: 0.3336 - val_accuracy: 0.2752 - val_loss: 3.0246
In [150]:
# check out loss curves of model_10
plot_loss_curves(history_10)
Well it seems our simplified model didn't work. Maybe we try data augmentation?
To do this, we need ImageDataGenerator instance. This time we'll add parameters such as rotation_range and horizontal_flip to manipulate our images.
In [152]:
# create augmented data generator instance
train_datagen_augmented = ImageDataGenerator(rescale=1/255.,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
train_data_augmented = train_datagen_augmented.flow_from_directory(train_dir,
target_size=(224,224),
batch_size=32,
class_mode='categorical')
Found 7500 images belonging to 10 classes.
After augmentation, we can try again with model_10. We don't have to rewrite the model, but use a handy tf function clone_model, which takes an existing model and rebuilt it in the same format.
The cloned model doesn't transport the learning the original model has done. So you basically train on the model with a clean slate.
Note: Key practices in deep learning is to be a serial experimenter. Trying something, see if it works, then try something else. A good experimenter also keeps track of all the things that were changed per step, and what results came of it. For our example, it's augmenting the data, and trying it on our previous model, to see if anything is changed from what we see on the loss curves
In [154]:
# clone the model
model_11 = tf.keras.models.clone_model(model_10)
# compile model
model_11.compile(loss='categorical_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
# fit the model
history_11 = model_11.fit(train_data_augmented,
epochs=5,
steps_per_epoch=len(train_data_augmented),
validation_data=test_data,
validation_steps=len(test_data))
Epoch 1/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 104s 439ms/step - accuracy: 0.1404 - loss: 2.4096 - val_accuracy: 0.2552 - val_loss: 2.0999 Epoch 2/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 104s 444ms/step - accuracy: 0.2399 - loss: 2.1277 - val_accuracy: 0.2788 - val_loss: 2.0456 Epoch 3/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 101s 429ms/step - accuracy: 0.2750 - loss: 2.0650 - val_accuracy: 0.3332 - val_loss: 1.9077 Epoch 4/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 101s 429ms/step - accuracy: 0.2827 - loss: 2.0143 - val_accuracy: 0.3288 - val_loss: 1.9321 Epoch 5/5 235/235 ━━━━━━━━━━━━━━━━━━━━ 102s 435ms/step - accuracy: 0.3084 - loss: 1.9931 - val_accuracy: 0.3700 - val_loss: 1.8488
You can see how much longer it takes, due to augmentation of images while training, and done using CPU rather than GPU.
Note: One way to imporve time taken, is use
tf.keras.layers.RandomFlipto flip images horizontally. Data loading can be speed up withtf.keras.utils.image_dataset_from_directory, which is an image loading API. (Will be covered later on)
So how's the model curves?
In [155]:
# check out model performance of model_11
plot_loss_curves(history_11)
Performance definitely looks better! Loss curves are better, and although augmented training test is quite low, validation dataset performed much better this time. Looks as if the model may continue to improve at a constant rate beyond our 5 epochs.
7. Repeat until satified¶
We can continue with things like restructuring model architevture, add more layers, adjust learning rate, different methods of augmentation etc. As you can guess, it takes a long ass time.
Good thing, there's the trick of transfer learning
We'll save that for the next notebook. But in the meantime, let's make a prediction with our trained multi-class model.
Making a prediction with our trained model¶
What good is a model, if you can't make predictions with it?
Let's remind ourselves the categories we're dealing with in the 10 categorical food items, before adding some custom images.
In [156]:
# what are our class names?
class_names
Out[156]:
array(['chicken_curry', 'chicken_wings', 'fried_rice', 'grilled_salmon',
'hamburger', 'ice_cream', 'pizza', 'ramen', 'steak', 'sushi'],
dtype='<U14')
Let's get some custom images.
And nowq we'll use pred_and_plot function to make prediction with model_11 on an image, and see it's output.
In [157]:
# make prediction on model_11 with custom image
pred_and_plot(model=model_11,
filename='03-steak.jpeg',
class_names=class_names)
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 1s/step
Not correct, but let's try another image
In [159]:
pred_and_plot(model=model_11,
filename='03-sushi.jpeg',
class_names=class_names)
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 86ms/step
still chicken curry :(
In [160]:
pred_and_plot(model_11, '03-pizza-dad.jpeg', class_names)
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 75ms/step
lmao chicken curry
maybe there's something wrong with the pred_and_plot function. Let's make a prediction outside of that function instead.
In [165]:
# load in and preprocess our custom image
img = load_and_prep_image('03-steak.jpeg')
# make prediction
pred = model_11.predict(tf.expand_dims(img,axis=0))
# match the prediction class to the highest prediction probability
pred_class=class_names[pred.argmax()]
plt.imshow(img)
plt.title(pred_class)
plt.axis(False);
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 65ms/step
The prediction is wrong, but now it's different.
The issue with our previous function, is most likely since it's only used for binary classification, and has no capability for handling multi classification data. Main problem lies in the prediction function.
In [167]:
# check the output of the predict function
pred = model_11.predict(tf.expand_dims(img,axis=0))
pred
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 75ms/step
Out[167]:
array([[0.03498406, 0.112686 , 0.070467 , 0.21361639, 0.08432788,
0.15790951, 0.02166959, 0.07102852, 0.19622356, 0.03708745]],
dtype=float32)
our model has softmax activation function with 10 output neurons, where each neurons is outputted a prediction probability.
We can use argmax to find through class_names, what was deemed most likely to be the food item from the model.
In [168]:
# find the predicted class name
class_names[pred.argmax()]
Out[168]:
np.str_('grilled_salmon')
Knowing that, we can readjust pred_and_plot function to work with multiple classes as well as binary classes.
In [169]:
# adjust function to work with multi-class
def pred_and_plot(model, filename, class_names):
'''
Imports an image located at filename, makes a prediction on it with
a trained model and plots the image with the predicted class as the title.
'''
# import the target image and preprocess it
img = load_and_prep_image(filename)
# make a prediction
pred = model.predict(tf.expand_dims(img,axis=0))
# get the predicted class
if len(pred[0]) > 1: # check for multiclass
pred_class = class_names[pred.argmax()] # if more than one output, take the max value
else:
pred_class = class_names[int(tf.round(pred)[0][0])] # if only one output, round up value that's > 0.5
# plot the image and predicted class
plt.imshow(img)
plt.title(f'Prediction: {pred_class}')
plt.axis(False);
let's try it out, now that it shouldn't show chicken curry
In [170]:
pred_and_plot(model_11, '03-steak.jpeg', class_names)
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 111ms/step
In [171]:
pred_and_plot(model_11, "03-sushi.jpeg", class_names)
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step
In [172]:
pred_and_plot(model_11, "03-pizza-dad.jpeg", class_names)
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
In [173]:
pred_and_plot(model_11, "03-hamburger.jpeg", class_names)
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step
unfortunate that the predictions aren't accurate, and only having around 35% accuracy in total with validation data.
We will improve on this later on through 'transfer learning'
Saving and loading our model¶
Once model is trained, you probably want to save it and load it elsewhere.
To do so, we can use save and load_model functions.
In [175]:
# save a model
model_11.save('saved_trained_model.keras')
In [176]:
# load in a model and evaluate it
loaded_model_11 = tf.keras.models.load_model('saved_trained_model.keras')
loaded_model_11.evaluate(test_data)
79/79 ━━━━━━━━━━━━━━━━━━━━ 27s 326ms/step - accuracy: 0.3824 - loss: 1.8630
Out[176]:
[1.848818302154541, 0.3700000047683716]
In [177]:
# compare saved model to unsaved model
model_11.evaluate(test_data)
79/79 ━━━━━━━━━━━━━━━━━━━━ 6s 76ms/step - accuracy: 0.3665 - loss: 1.8623
Out[177]:
[1.8488181829452515, 0.3700000047683716]
Exercises¶
- Check out CNN explainer website, and note the keyy terms/explain convolution, pooling, etc. in your own words.
What is CNN?¶
Its a type of classifier, suited to image prediction. You have an input of a tensor (n-dimensions of a matrix/dataset), with neurons that have inputs, which all yield to a singular output. These can then create a filter that's applied on top of the image to enhance or select important features of the image. In addition to weights and biases on every neuron, to tweak if prediction from model is off.
How does a convolutional layer work?¶
It starts with the kernel size, aka a grid size, with weights attached to it, to help it discern important details of an image. The weights apply a multiplication value to each pixel in the set grid, say 3x3, and all pixel values that's multiplied by weight, is then outputted as a single value when added together. This becomes the new value of our first pixel, and this continues for the next pixel. This is known as a dot product of 1. If its 2, it moves to every 2 pixels.
Due to images having rgb channels, making 3 separate images, the 3 image values are added up together as a final output, but with bias added, based on what the model think will help prediction accuracy.
Each of these filters are there to detect specific features, such as edges, eyes, curves, etc.
ReLU and Softmax¶
ReLU is widely used for its non-linearity predictions. Which makes it great for complex models, because not every prediction is a linear straight line. How it does it, is it keeps all positive values the same, but returns all negative values as 0.
Softmax is typically used on multi classification problems. When there is an output of more than 2 classes, they don't add up neatly to 1. Softmax does a sort of normalization/regularization to all the values, so that all classes will add up to 1, aka 100%.
Max Pooling¶
Operates by shrinking image size, but simultaneously keeping the most important information within. Typically a 2x2 grid area, it often looks for the largest value in that grid area/kernel slice. Then can either move next pixel, or second pixel etc.
In [1]:
pip install nbconvert PyPDF2
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In [ ]: